Overview

Dataset statistics

Number of variables21
Number of observations4119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory675.9 KiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

default is highly imbalanced (54.9%)Imbalance
poutcome is highly imbalanced (55.1%)Imbalance
y is highly imbalanced (50.2%)Imbalance
previous has 3523 (85.5%) zerosZeros

Reproduction

Analysis started2024-06-15 18:18:33.252666
Analysis finished2024-06-15 18:19:09.751701
Duration36.5 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct67
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.11362
Minimum18
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:10.096177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum88
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.313362
Coefficient of variation (CV)0.25710374
Kurtosis0.43812976
Mean40.11362
Median Absolute Deviation (MAD)7
Skewness0.71569398
Sum165228
Variance106.36543
MonotonicityNot monotonic
2024-06-15T23:49:10.581098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 216
 
5.2%
31 191
 
4.6%
30 177
 
4.3%
34 174
 
4.2%
35 172
 
4.2%
33 170
 
4.1%
36 168
 
4.1%
38 150
 
3.6%
41 147
 
3.6%
29 139
 
3.4%
Other values (57) 2415
58.6%
ValueCountFrequency (%)
18 3
 
0.1%
19 1
 
< 0.1%
20 4
 
0.1%
21 7
 
0.2%
22 10
 
0.2%
23 16
 
0.4%
24 57
1.4%
25 57
1.4%
26 62
1.5%
27 87
2.1%
ValueCountFrequency (%)
88 1
 
< 0.1%
86 2
< 0.1%
85 1
 
< 0.1%
82 2
< 0.1%
81 3
0.1%
80 4
0.1%
78 3
0.1%
77 2
< 0.1%
76 4
0.1%
75 2
< 0.1%

job
Categorical

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
admin.
1012 
blue-collar
884 
technician
691 
services
393 
management
324 
Other values (7)
815 

Length

Max length13
Median length12
Mean length8.9929595
Min length6

Characters and Unicode

Total characters37042
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue-collar
2nd rowservices
3rd rowservices
4th rowservices
5th rowadmin.

Common Values

ValueCountFrequency (%)
admin. 1012
24.6%
blue-collar 884
21.5%
technician 691
16.8%
services 393
 
9.5%
management 324
 
7.9%
retired 166
 
4.0%
self-employed 159
 
3.9%
entrepreneur 148
 
3.6%
unemployed 111
 
2.7%
housemaid 110
 
2.7%
Other values (2) 121
 
2.9%

Length

2024-06-15T23:49:11.051049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 1012
24.6%
blue-collar 884
21.5%
technician 691
16.8%
services 393
 
9.5%
management 324
 
7.9%
retired 166
 
4.0%
self-employed 159
 
3.9%
entrepreneur 148
 
3.6%
unemployed 111
 
2.7%
housemaid 110
 
2.7%
Other values (2) 121
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 4824
13.0%
n 3648
 
9.8%
a 3345
 
9.0%
l 3081
 
8.3%
i 3063
 
8.3%
c 2659
 
7.2%
r 2053
 
5.5%
m 2040
 
5.5%
d 1640
 
4.4%
t 1493
 
4.0%
Other values (14) 9196
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34987
94.5%
Dash Punctuation 1043
 
2.8%
Other Punctuation 1012
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4824
13.8%
n 3648
10.4%
a 3345
9.6%
l 3081
8.8%
i 3063
8.8%
c 2659
 
7.6%
r 2053
 
5.9%
m 2040
 
5.8%
d 1640
 
4.7%
t 1493
 
4.3%
Other values (12) 7141
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 1043
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34987
94.5%
Common 2055
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4824
13.8%
n 3648
10.4%
a 3345
9.6%
l 3081
8.8%
i 3063
8.8%
c 2659
 
7.6%
r 2053
 
5.9%
m 2040
 
5.8%
d 1640
 
4.7%
t 1493
 
4.3%
Other values (12) 7141
20.4%
Common
ValueCountFrequency (%)
- 1043
50.8%
. 1012
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4824
13.0%
n 3648
 
9.8%
a 3345
 
9.0%
l 3081
 
8.3%
i 3063
 
8.3%
c 2659
 
7.2%
r 2053
 
5.5%
m 2040
 
5.5%
d 1640
 
4.4%
t 1493
 
4.0%
Other values (14) 9196
24.8%

marital
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
married
2509 
single
1153 
divorced
446 
unknown
 
11

Length

Max length8
Median length7
Mean length6.8283564
Min length6

Characters and Unicode

Total characters28126
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 2509
60.9%
single 1153
28.0%
divorced 446
 
10.8%
unknown 11
 
0.3%

Length

2024-06-15T23:49:11.504505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:12.536620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 2509
60.9%
single 1153
28.0%
divorced 446
 
10.8%
unknown 11
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 5464
19.4%
i 4108
14.6%
e 4108
14.6%
d 3401
12.1%
m 2509
8.9%
a 2509
8.9%
n 1186
 
4.2%
s 1153
 
4.1%
g 1153
 
4.1%
l 1153
 
4.1%
Other values (6) 1382
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28126
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5464
19.4%
i 4108
14.6%
e 4108
14.6%
d 3401
12.1%
m 2509
8.9%
a 2509
8.9%
n 1186
 
4.2%
s 1153
 
4.1%
g 1153
 
4.1%
l 1153
 
4.1%
Other values (6) 1382
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 28126
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5464
19.4%
i 4108
14.6%
e 4108
14.6%
d 3401
12.1%
m 2509
8.9%
a 2509
8.9%
n 1186
 
4.2%
s 1153
 
4.1%
g 1153
 
4.1%
l 1153
 
4.1%
Other values (6) 1382
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 5464
19.4%
i 4108
14.6%
e 4108
14.6%
d 3401
12.1%
m 2509
8.9%
a 2509
8.9%
n 1186
 
4.2%
s 1153
 
4.1%
g 1153
 
4.1%
l 1153
 
4.1%
Other values (6) 1382
 
4.9%

education
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
university.degree
1264 
high.school
921 
basic.9y
574 
professional.course
535 
basic.4y
429 
Other values (3)
396 

Length

Max length19
Median length17
Mean length12.821316
Min length7

Characters and Unicode

Total characters52811
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbasic.9y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.9y
5th rowuniversity.degree

Common Values

ValueCountFrequency (%)
university.degree 1264
30.7%
high.school 921
22.4%
basic.9y 574
13.9%
professional.course 535
13.0%
basic.4y 429
 
10.4%
basic.6y 228
 
5.5%
unknown 167
 
4.1%
illiterate 1
 
< 0.1%

Length

2024-06-15T23:49:12.962331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:13.378036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 1264
30.7%
high.school 921
22.4%
basic.9y 574
13.9%
professional.course 535
13.0%
basic.4y 429
 
10.4%
basic.6y 228
 
5.5%
unknown 167
 
4.1%
illiterate 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 6128
11.6%
i 5217
 
9.9%
s 5021
 
9.5%
. 3951
 
7.5%
o 3614
 
6.8%
r 3599
 
6.8%
h 2763
 
5.2%
c 2687
 
5.1%
y 2495
 
4.7%
n 2300
 
4.4%
Other values (15) 15036
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47629
90.2%
Other Punctuation 3951
 
7.5%
Decimal Number 1231
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6128
12.9%
i 5217
11.0%
s 5021
10.5%
o 3614
 
7.6%
r 3599
 
7.6%
h 2763
 
5.8%
c 2687
 
5.6%
y 2495
 
5.2%
n 2300
 
4.8%
g 2185
 
4.6%
Other values (11) 11620
24.4%
Decimal Number
ValueCountFrequency (%)
9 574
46.6%
4 429
34.8%
6 228
 
18.5%
Other Punctuation
ValueCountFrequency (%)
. 3951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47629
90.2%
Common 5182
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6128
12.9%
i 5217
11.0%
s 5021
10.5%
o 3614
 
7.6%
r 3599
 
7.6%
h 2763
 
5.8%
c 2687
 
5.6%
y 2495
 
5.2%
n 2300
 
4.8%
g 2185
 
4.6%
Other values (11) 11620
24.4%
Common
ValueCountFrequency (%)
. 3951
76.2%
9 574
 
11.1%
4 429
 
8.3%
6 228
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6128
11.6%
i 5217
 
9.9%
s 5021
 
9.5%
. 3951
 
7.5%
o 3614
 
6.8%
r 3599
 
6.8%
h 2763
 
5.2%
c 2687
 
5.1%
y 2495
 
4.7%
n 2300
 
4.4%
Other values (15) 15036
28.5%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
no
3315 
unknown
803 
yes
 
1

Length

Max length7
Median length2
Mean length2.9749939
Min length2

Characters and Unicode

Total characters12254
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 3315
80.5%
unknown 803
 
19.5%
yes 1
 
< 0.1%

Length

2024-06-15T23:49:13.878035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:14.221784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 3315
80.5%
unknown 803
 
19.5%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 5724
46.7%
o 4118
33.6%
u 803
 
6.6%
k 803
 
6.6%
w 803
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12254
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5724
46.7%
o 4118
33.6%
u 803
 
6.6%
k 803
 
6.6%
w 803
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 12254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5724
46.7%
o 4118
33.6%
u 803
 
6.6%
k 803
 
6.6%
w 803
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5724
46.7%
o 4118
33.6%
u 803
 
6.6%
k 803
 
6.6%
w 803
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

housing
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
yes
2175 
no
1839 
unknown
 
105

Length

Max length7
Median length3
Mean length2.6554989
Min length2

Characters and Unicode

Total characters10938
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowyes
4th rowunknown
5th rowyes

Common Values

ValueCountFrequency (%)
yes 2175
52.8%
no 1839
44.6%
unknown 105
 
2.5%

Length

2024-06-15T23:49:14.612411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:14.948917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 2175
52.8%
no 1839
44.6%
unknown 105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
y 2175
19.9%
e 2175
19.9%
s 2175
19.9%
n 2154
19.7%
o 1944
17.8%
u 105
 
1.0%
k 105
 
1.0%
w 105
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10938
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 2175
19.9%
e 2175
19.9%
s 2175
19.9%
n 2154
19.7%
o 1944
17.8%
u 105
 
1.0%
k 105
 
1.0%
w 105
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10938
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 2175
19.9%
e 2175
19.9%
s 2175
19.9%
n 2154
19.7%
o 1944
17.8%
u 105
 
1.0%
k 105
 
1.0%
w 105
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 2175
19.9%
e 2175
19.9%
s 2175
19.9%
n 2154
19.7%
o 1944
17.8%
u 105
 
1.0%
k 105
 
1.0%
w 105
 
1.0%

loan
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
no
3349 
yes
665 
unknown
 
105

Length

Max length7
Median length2
Mean length2.2889051
Min length2

Characters and Unicode

Total characters9428
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowunknown
5th rowno

Common Values

ValueCountFrequency (%)
no 3349
81.3%
yes 665
 
16.1%
unknown 105
 
2.5%

Length

2024-06-15T23:49:15.323917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:15.667667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 3349
81.3%
yes 665
 
16.1%
unknown 105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n 3664
38.9%
o 3454
36.6%
y 665
 
7.1%
e 665
 
7.1%
s 665
 
7.1%
u 105
 
1.1%
k 105
 
1.1%
w 105
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9428
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3664
38.9%
o 3454
36.6%
y 665
 
7.1%
e 665
 
7.1%
s 665
 
7.1%
u 105
 
1.1%
k 105
 
1.1%
w 105
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3664
38.9%
o 3454
36.6%
y 665
 
7.1%
e 665
 
7.1%
s 665
 
7.1%
u 105
 
1.1%
k 105
 
1.1%
w 105
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3664
38.9%
o 3454
36.6%
y 665
 
7.1%
e 665
 
7.1%
s 665
 
7.1%
u 105
 
1.1%
k 105
 
1.1%
w 105
 
1.1%

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
cellular
2652 
telephone
1467 

Length

Max length9
Median length8
Mean length8.3561544
Min length8

Characters and Unicode

Total characters34419
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 2652
64.4%
telephone 1467
35.6%

Length

2024-06-15T23:49:16.042668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:16.386417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 2652
64.4%
telephone 1467
35.6%

Most occurring characters

ValueCountFrequency (%)
l 9423
27.4%
e 7053
20.5%
c 2652
 
7.7%
u 2652
 
7.7%
a 2652
 
7.7%
r 2652
 
7.7%
t 1467
 
4.3%
p 1467
 
4.3%
h 1467
 
4.3%
o 1467
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34419
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 9423
27.4%
e 7053
20.5%
c 2652
 
7.7%
u 2652
 
7.7%
a 2652
 
7.7%
r 2652
 
7.7%
t 1467
 
4.3%
p 1467
 
4.3%
h 1467
 
4.3%
o 1467
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 34419
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 9423
27.4%
e 7053
20.5%
c 2652
 
7.7%
u 2652
 
7.7%
a 2652
 
7.7%
r 2652
 
7.7%
t 1467
 
4.3%
p 1467
 
4.3%
h 1467
 
4.3%
o 1467
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 9423
27.4%
e 7053
20.5%
c 2652
 
7.7%
u 2652
 
7.7%
a 2652
 
7.7%
r 2652
 
7.7%
t 1467
 
4.3%
p 1467
 
4.3%
h 1467
 
4.3%
o 1467
 
4.3%

month
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
may
1378 
jul
711 
aug
636 
jun
530 
nov
446 
Other values (5)
418 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12357
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowjun
4th rowjun
5th rownov

Common Values

ValueCountFrequency (%)
may 1378
33.5%
jul 711
17.3%
aug 636
15.4%
jun 530
 
12.9%
nov 446
 
10.8%
apr 215
 
5.2%
oct 69
 
1.7%
sep 64
 
1.6%
mar 48
 
1.2%
dec 22
 
0.5%

Length

2024-06-15T23:49:16.808705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:17.261832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 1378
33.5%
jul 711
17.3%
aug 636
15.4%
jun 530
 
12.9%
nov 446
 
10.8%
apr 215
 
5.2%
oct 69
 
1.7%
sep 64
 
1.6%
mar 48
 
1.2%
dec 22
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 2277
18.4%
u 1877
15.2%
m 1426
11.5%
y 1378
11.2%
j 1241
10.0%
n 976
7.9%
l 711
 
5.8%
g 636
 
5.1%
o 515
 
4.2%
v 446
 
3.6%
Other values (7) 874
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2277
18.4%
u 1877
15.2%
m 1426
11.5%
y 1378
11.2%
j 1241
10.0%
n 976
7.9%
l 711
 
5.8%
g 636
 
5.1%
o 515
 
4.2%
v 446
 
3.6%
Other values (7) 874
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 12357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2277
18.4%
u 1877
15.2%
m 1426
11.5%
y 1378
11.2%
j 1241
10.0%
n 976
7.9%
l 711
 
5.8%
g 636
 
5.1%
o 515
 
4.2%
v 446
 
3.6%
Other values (7) 874
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2277
18.4%
u 1877
15.2%
m 1426
11.5%
y 1378
11.2%
j 1241
10.0%
n 976
7.9%
l 711
 
5.8%
g 636
 
5.1%
o 515
 
4.2%
v 446
 
3.6%
Other values (7) 874
 
7.1%

day_of_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
thu
860 
mon
855 
tue
841 
wed
795 
fri
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12357
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowfri
3rd rowwed
4th rowfri
5th rowmon

Common Values

ValueCountFrequency (%)
thu 860
20.9%
mon 855
20.8%
tue 841
20.4%
wed 795
19.3%
fri 768
18.6%

Length

2024-06-15T23:49:17.730997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:18.106420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 860
20.9%
mon 855
20.8%
tue 841
20.4%
wed 795
19.3%
fri 768
18.6%

Most occurring characters

ValueCountFrequency (%)
t 1701
13.8%
u 1701
13.8%
e 1636
13.2%
h 860
7.0%
m 855
6.9%
o 855
6.9%
n 855
6.9%
w 795
6.4%
d 795
6.4%
f 768
6.2%
Other values (2) 1536
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1701
13.8%
u 1701
13.8%
e 1636
13.2%
h 860
7.0%
m 855
6.9%
o 855
6.9%
n 855
6.9%
w 795
6.4%
d 795
6.4%
f 768
6.2%
Other values (2) 1536
12.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 12357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1701
13.8%
u 1701
13.8%
e 1636
13.2%
h 860
7.0%
m 855
6.9%
o 855
6.9%
n 855
6.9%
w 795
6.4%
d 795
6.4%
f 768
6.2%
Other values (2) 1536
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1701
13.8%
u 1701
13.8%
e 1636
13.2%
h 860
7.0%
m 855
6.9%
o 855
6.9%
n 855
6.9%
w 795
6.4%
d 795
6.4%
f 768
6.2%
Other values (2) 1536
12.4%

duration
Real number (ℝ)

Distinct828
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.78806
Minimum0
Maximum3643
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:18.559544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median181
Q3317
95-th percentile740.2
Maximum3643
Range3643
Interquartile range (IQR)214

Descriptive statistics

Standard deviation254.70374
Coefficient of variation (CV)0.99188311
Kurtosis20.761929
Mean256.78806
Median Absolute Deviation (MAD)92
Skewness3.2947813
Sum1057710
Variance64873.993
MonotonicityNot monotonic
2024-06-15T23:49:19.028357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 24
 
0.6%
112 23
 
0.6%
73 22
 
0.5%
81 21
 
0.5%
122 20
 
0.5%
113 20
 
0.5%
90 20
 
0.5%
145 20
 
0.5%
83 20
 
0.5%
114 19
 
0.5%
Other values (818) 3910
94.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
4 1
 
< 0.1%
5 4
 
0.1%
6 5
0.1%
7 4
 
0.1%
8 6
0.1%
9 9
0.2%
10 10
0.2%
11 8
0.2%
12 6
0.1%
ValueCountFrequency (%)
3643 1
< 0.1%
3253 1
< 0.1%
2653 1
< 0.1%
2301 1
< 0.1%
1980 1
< 0.1%
1868 1
< 0.1%
1855 2
< 0.1%
1820 1
< 0.1%
1806 1
< 0.1%
1720 1
< 0.1%

campaign
Real number (ℝ)

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5372663
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:19.450228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum35
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5681592
Coefficient of variation (CV)1.0121757
Kurtosis25.28452
Mean2.5372663
Median Absolute Deviation (MAD)1
Skewness4.003185
Sum10451
Variance6.5954419
MonotonicityNot monotonic
2024-06-15T23:49:19.856476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1764
42.8%
2 1039
25.2%
3 549
 
13.3%
4 291
 
7.1%
5 142
 
3.4%
6 99
 
2.4%
7 60
 
1.5%
8 36
 
0.9%
9 32
 
0.8%
10 20
 
0.5%
Other values (15) 87
 
2.1%
ValueCountFrequency (%)
1 1764
42.8%
2 1039
25.2%
3 549
 
13.3%
4 291
 
7.1%
5 142
 
3.4%
6 99
 
2.4%
7 60
 
1.5%
8 36
 
0.9%
9 32
 
0.8%
10 20
 
0.5%
ValueCountFrequency (%)
35 1
 
< 0.1%
29 2
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
23 2
 
< 0.1%
22 2
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 14
0.3%
16 7
0.2%

pdays
Real number (ℝ)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean960.42219
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:20.247107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.92279
Coefficient of variation (CV)0.19983169
Kurtosis20.812484
Mean960.42219
Median Absolute Deviation (MAD)0
Skewness-4.7751392
Sum3955979
Variance36834.356
MonotonicityNot monotonic
2024-06-15T23:49:20.637728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
999 3959
96.1%
3 52
 
1.3%
6 42
 
1.0%
4 14
 
0.3%
7 10
 
0.2%
10 8
 
0.2%
12 5
 
0.1%
5 4
 
0.1%
2 4
 
0.1%
1 3
 
0.1%
Other values (11) 18
 
0.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 3
 
0.1%
2 4
 
0.1%
3 52
1.3%
4 14
 
0.3%
5 4
 
0.1%
6 42
1.0%
7 10
 
0.2%
9 3
 
0.1%
10 8
 
0.2%
ValueCountFrequency (%)
999 3959
96.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
0.1%

previous
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19033746
Minimum0
Maximum6
Zeros3523
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:20.966171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54178832
Coefficient of variation (CV)2.8464619
Kurtosis22.120323
Mean0.19033746
Median Absolute Deviation (MAD)0
Skewness4.0229788
Sum784
Variance0.29353459
MonotonicityNot monotonic
2024-06-15T23:49:21.325550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3523
85.5%
1 475
 
11.5%
2 78
 
1.9%
3 25
 
0.6%
4 14
 
0.3%
5 2
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 3523
85.5%
1 475
 
11.5%
2 78
 
1.9%
3 25
 
0.6%
4 14
 
0.3%
5 2
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 2
 
< 0.1%
4 14
 
0.3%
3 25
 
0.6%
2 78
 
1.9%
1 475
 
11.5%
0 3523
85.5%

poutcome
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
nonexistent
3523 
failure
454 
success
 
142

Length

Max length11
Median length11
Mean length10.421219
Min length7

Characters and Unicode

Total characters42925
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 3523
85.5%
failure 454
 
11.0%
success 142
 
3.4%

Length

2024-06-15T23:49:21.763046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-15T23:49:22.153677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 3523
85.5%
failure 454
 
11.0%
success 142
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 10569
24.6%
e 7642
17.8%
t 7046
16.4%
i 3977
 
9.3%
s 3949
 
9.2%
o 3523
 
8.2%
x 3523
 
8.2%
u 596
 
1.4%
f 454
 
1.1%
a 454
 
1.1%
Other values (3) 1192
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42925
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10569
24.6%
e 7642
17.8%
t 7046
16.4%
i 3977
 
9.3%
s 3949
 
9.2%
o 3523
 
8.2%
x 3523
 
8.2%
u 596
 
1.4%
f 454
 
1.1%
a 454
 
1.1%
Other values (3) 1192
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 42925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10569
24.6%
e 7642
17.8%
t 7046
16.4%
i 3977
 
9.3%
s 3949
 
9.2%
o 3523
 
8.2%
x 3523
 
8.2%
u 596
 
1.4%
f 454
 
1.1%
a 454
 
1.1%
Other values (3) 1192
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10569
24.6%
e 7642
17.8%
t 7046
16.4%
i 3977
 
9.3%
s 3949
 
9.2%
o 3523
 
8.2%
x 3523
 
8.2%
u 596
 
1.4%
f 454
 
1.1%
a 454
 
1.1%
Other values (3) 1192
 
2.8%

emp.var.rate
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084972081
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative1735
Negative (%)42.1%
Memory size32.3 KiB
2024-06-15T23:49:22.466179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5631145
Coefficient of variation (CV)18.395624
Kurtosis-1.0417839
Mean0.084972081
Median Absolute Deviation (MAD)0.3
Skewness-0.72768788
Sum350
Variance2.4433268
MonotonicityNot monotonic
2024-06-15T23:49:22.809924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 1626
39.5%
-1.8 883
21.4%
1.1 758
18.4%
-0.1 392
 
9.5%
-2.9 164
 
4.0%
-3.4 104
 
2.5%
-1.7 87
 
2.1%
-1.1 83
 
2.0%
-3 21
 
0.5%
-0.2 1
 
< 0.1%
ValueCountFrequency (%)
-3.4 104
 
2.5%
-3 21
 
0.5%
-2.9 164
 
4.0%
-1.8 883
21.4%
-1.7 87
 
2.1%
-1.1 83
 
2.0%
-0.2 1
 
< 0.1%
-0.1 392
 
9.5%
1.1 758
18.4%
1.4 1626
39.5%
ValueCountFrequency (%)
1.4 1626
39.5%
1.1 758
18.4%
-0.1 392
 
9.5%
-0.2 1
 
< 0.1%
-1.1 83
 
2.0%
-1.7 87
 
2.1%
-1.8 883
21.4%
-2.9 164
 
4.0%
-3 21
 
0.5%
-3.4 104
 
2.5%

cons.price.idx
Real number (ℝ)

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579704
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:23.153667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5793488
Coefficient of variation (CV)0.0061909664
Kurtosis-0.82335789
Mean93.579704
Median Absolute Deviation (MAD)0.38
Skewness-0.21664142
Sum385454.8
Variance0.33564504
MonotonicityNot monotonic
2024-06-15T23:49:23.559925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 758
18.4%
93.918 667
16.2%
92.893 597
14.5%
93.444 528
12.8%
94.465 431
10.5%
93.2 386
9.4%
93.075 201
 
4.9%
92.963 75
 
1.8%
92.201 75
 
1.8%
92.431 43
 
1.0%
Other values (16) 358
8.7%
ValueCountFrequency (%)
92.201 75
 
1.8%
92.379 25
 
0.6%
92.431 43
 
1.0%
92.469 14
 
0.3%
92.649 36
 
0.9%
92.713 21
 
0.5%
92.756 1
 
< 0.1%
92.843 25
 
0.6%
92.893 597
14.5%
92.963 75
 
1.8%
ValueCountFrequency (%)
94.767 24
 
0.6%
94.601 20
 
0.5%
94.465 431
10.5%
94.215 30
 
0.7%
94.199 39
 
0.9%
94.055 24
 
0.6%
94.027 33
 
0.8%
93.994 758
18.4%
93.918 667
16.2%
93.876 23
 
0.6%

cons.conf.idx
Real number (ℝ)

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.499102
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative4119
Negative (%)100.0%
Memory size32.3 KiB
2024-06-15T23:49:23.934921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.5945775
Coefficient of variation (CV)-0.11344887
Kurtosis-0.314303
Mean-40.499102
Median Absolute Deviation (MAD)4.4
Skewness0.28730908
Sum-166815.8
Variance21.110142
MonotonicityNot monotonic
2024-06-15T23:49:24.325546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 758
18.4%
-42.7 667
16.2%
-46.2 597
14.5%
-36.1 528
12.8%
-41.8 431
10.5%
-42 386
9.4%
-47.1 201
 
4.9%
-40.8 75
 
1.8%
-31.4 75
 
1.8%
-26.9 43
 
1.0%
Other values (16) 358
8.7%
ValueCountFrequency (%)
-50.8 24
 
0.6%
-50 25
 
0.6%
-49.5 20
 
0.5%
-47.1 201
 
4.9%
-46.2 597
14.5%
-45.9 1
 
< 0.1%
-42.7 667
16.2%
-42 386
9.4%
-41.8 431
10.5%
-40.8 75
 
1.8%
ValueCountFrequency (%)
-26.9 43
 
1.0%
-29.8 25
 
0.6%
-30.1 36
 
0.9%
-31.4 75
 
1.8%
-33 21
 
0.5%
-33.6 14
 
0.3%
-34.6 14
 
0.3%
-34.8 23
 
0.6%
-36.1 528
12.8%
-36.4 758
18.4%

euribor3m
Real number (ℝ)

Distinct234
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6213557
Minimum0.635
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:24.763054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.635
5-th percentile0.8084
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.41
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.7335912
Coefficient of variation (CV)0.47871333
Kurtosis-1.3963663
Mean3.6213557
Median Absolute Deviation (MAD)0.108
Skewness-0.71507987
Sum14916.364
Variance3.0053385
MonotonicityNot monotonic
2024-06-15T23:49:25.247647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 274
 
6.7%
4.963 256
 
6.2%
4.962 237
 
5.8%
4.961 212
 
5.1%
4.856 138
 
3.4%
4.965 114
 
2.8%
4.964 110
 
2.7%
1.405 106
 
2.6%
4.96 105
 
2.5%
4.968 101
 
2.5%
Other values (224) 2466
59.9%
ValueCountFrequency (%)
0.635 3
0.1%
0.636 1
 
< 0.1%
0.637 1
 
< 0.1%
0.639 2
 
< 0.1%
0.64 1
 
< 0.1%
0.642 1
 
< 0.1%
0.643 2
 
< 0.1%
0.644 5
0.1%
0.645 2
 
< 0.1%
0.646 4
0.1%
ValueCountFrequency (%)
5.045 1
 
< 0.1%
4.97 21
 
0.5%
4.968 101
 
2.5%
4.967 62
 
1.5%
4.966 72
 
1.7%
4.965 114
2.8%
4.964 110
2.7%
4.963 256
6.2%
4.962 237
5.8%
4.961 212
5.1%

nr.employed
Real number (ℝ)

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5166.4817
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2024-06-15T23:49:25.622643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation73.667904
Coefficient of variation (CV)0.014258814
Kurtosis0.061724198
Mean5166.4817
Median Absolute Deviation (MAD)37.1
Skewness-1.0758769
Sum21280738
Variance5426.96
MonotonicityNot monotonic
2024-06-15T23:49:25.966392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 1626
39.5%
5099.1 823
20.0%
5191 758
18.4%
5195.8 392
 
9.5%
5076.2 164
 
4.0%
5017.5 104
 
2.5%
4991.6 87
 
2.1%
4963.6 83
 
2.0%
5008.7 60
 
1.5%
5023.5 21
 
0.5%
ValueCountFrequency (%)
4963.6 83
 
2.0%
4991.6 87
 
2.1%
5008.7 60
 
1.5%
5017.5 104
 
2.5%
5023.5 21
 
0.5%
5076.2 164
 
4.0%
5099.1 823
20.0%
5176.3 1
 
< 0.1%
5191 758
18.4%
5195.8 392
9.5%
ValueCountFrequency (%)
5228.1 1626
39.5%
5195.8 392
 
9.5%
5191 758
18.4%
5176.3 1
 
< 0.1%
5099.1 823
20.0%
5076.2 164
 
4.0%
5023.5 21
 
0.5%
5017.5 104
 
2.5%
5008.7 60
 
1.5%
4991.6 87
 
2.1%

y
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
False
3668 
True
451 
ValueCountFrequency (%)
False 3668
89.1%
True 451
 
10.9%
2024-06-15T23:49:26.310521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-06-15T23:49:05.180140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:36.170950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:39.910799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:43.218526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:46.430697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:49.545363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:52.775357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:55.857477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:58.976484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:02.113827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:05.500293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:36.552639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:40.261731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:43.554352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:46.766155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:49.879904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:53.117031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:56.189552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:59.293079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:02.437200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:05.838243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:37.092777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:40.585026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:43.903461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:47.097299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:50.215931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:53.437740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:56.531896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:59.621478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:02.767601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:06.157491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:37.582634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:40.935970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:44.234715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:47.426030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:50.549744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:53.755233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:56.859084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:59.955023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:03.108450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:06.457618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:38.017216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:41.268407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:44.547933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:47.714309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:50.943815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:54.056024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:57.156118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:00.262581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:03.400796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:06.774076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:38.342365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:41.599197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:44.872654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:48.044501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:51.256223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:54.367383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:57.471603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:00.585098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:03.706258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:07.066298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:38.666869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:41.916163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:45.181433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:48.341853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:51.554805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:54.670888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:57.773698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:00.906050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:03.991544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:07.367688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:38.985293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:42.248739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:45.495976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:48.646932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:51.862253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:54.964729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:58.076766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:01.215186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:04.290978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:07.666371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:39.293741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:42.574504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:45.817059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:48.946550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:52.162516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:55.257449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:58.369096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:01.512679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:04.588991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:07.956829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:39.597798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:42.898190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:46.123521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:49.246719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:52.461920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:55.550344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:48:58.679862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:01.817994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-06-15T23:49:04.869611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-06-15T23:49:08.439344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-15T23:49:09.382466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
030blue-collarmarriedbasic.9ynoyesnocellularmayfri48729990nonexistent-1.892.893-46.21.3135099.1no
139servicessinglehigh.schoolnononotelephonemayfri34649990nonexistent1.193.994-36.44.8555191.0no
225servicesmarriedhigh.schoolnoyesnotelephonejunwed22719990nonexistent1.494.465-41.84.9625228.1no
338servicesmarriedbasic.9ynounknownunknowntelephonejunfri1739990nonexistent1.494.465-41.84.9595228.1no
447admin.marrieduniversity.degreenoyesnocellularnovmon5819990nonexistent-0.193.200-42.04.1915195.8no
532servicessingleuniversity.degreenononocellularsepthu12839992failure-1.194.199-37.50.8844963.6no
632admin.singleuniversity.degreenoyesnocellularsepmon29049990nonexistent-1.194.199-37.50.8794963.6no
741entrepreneurmarrieduniversity.degreeunknownyesnocellularnovmon4429990nonexistent-0.193.200-42.04.1915195.8no
831servicesdivorcedprofessional.coursenononocellularnovtue6819991failure-0.193.200-42.04.1535195.8no
935blue-collarmarriedbasic.9yunknownnonotelephonemaythu17019990nonexistent1.193.994-36.44.8555191.0no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
410963retiredmarriedhigh.schoolnononocellularoctwed138619990nonexistent-3.492.431-26.90.7405017.5no
411053housemaiddivorcedbasic.6yunknownunknownunknowntelephonemayfri8529990nonexistent1.193.994-36.44.8555191.0no
411130technicianmarrieduniversity.degreenonoyescellularjunfri13119991failure-1.794.055-39.80.7484991.6no
411231techniciansingleprofessional.coursenoyesnocellularnovthu15519990nonexistent-0.193.200-42.04.0765195.8no
411331admin.singleuniversity.degreenoyesnocellularnovthu46319990nonexistent-0.193.200-42.04.0765195.8no
411430admin.marriedbasic.6ynoyesyescellularjulthu5319990nonexistent1.493.918-42.74.9585228.1no
411539admin.marriedhigh.schoolnoyesnotelephonejulfri21919990nonexistent1.493.918-42.74.9595228.1no
411627studentsinglehigh.schoolnononocellularmaymon6429991failure-1.892.893-46.21.3545099.1no
411758admin.marriedhigh.schoolnononocellularaugfri52819990nonexistent1.493.444-36.14.9665228.1no
411834managementsinglehigh.schoolnoyesnocellularnovwed17519990nonexistent-0.193.200-42.04.1205195.8no